Data insufficiency is another core challenge in the healthcare domain. Since data collection is costly in medical centers, the volume of data used to train models is usually limited, making it challenging to achieve adequate performance.
The data distribution of different medical centers can vary greatly. Thus training a model over the multi-center dataset cannot lead to a satisfactory average or separate prediction result.
Based on this observation, decoupling multi-center diabetes prediction and separately considering prediction tasks for each center is necessary.
Unfortunately, due to the data insufficiency, it is still hard to train high-performance models for every single center.
Multi-task learning (MTL) [1]} aggregates knowledge from different tasks to train a high-performance model. Based on this consideration, we are able to treat predictions for a single center as separate tasks and build the model leveraging knowledge shared among them to improve prediction results.
